Essays about: "Variational Auto-Encoder"

Showing result 1 - 5 of 11 essays containing the words Variational Auto-Encoder.

1. 1. Towards topology-aware Variational Auto-Encoders : from InvMap-VAE to Witness Simplicial VAE

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Aniss Aiman Medbouhi; [2022]
   Keywords : Variational Auto-Encoder; Nonlinear dimensionality reduction; Generative model; Inverse projection; Computational topology; Algorithmic topology; Topological Data Analysis; Data visualisation; Unsupervised representation learning; Topological machine learning; Betti number; Simplicial complex; Witness complex; Simplicial map; Simplicial regularization.; Variations autokodare; Ickelinjär dimensionalitetsreducering; Generativ modell; Invers projektion; Beräkningstopologi; Algoritmisk topologi; Topologisk Data Analys; Datavisualisering; Oövervakat representationsinlärning; Topologisk maskininlärning; Betti-nummer; Simplicielt komplex; Vittneskomplex; Simpliciel avbildning; Simpliciel regularisering.;

   Abstract : Variational Auto-Encoders (VAEs) are one of the most famous deep generative models. After showing that standard VAEs may not preserve the topology, that is the shape of the data, between the input and the latent space, we tried to modify them so that the topology is preserved. READ MORE

3. 2. Attribute Embedding for Variational Auto-Encoders : Regularization derived from triplet loss

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Anton E. L. Dahlin; [2022]
   Keywords : Variational Auto-Encoder; Triplet Loss; Contrastive Loss; Generative Models; Metric Learning; Latent Space; Attribute Manipulation; Variationsautokodare; Triplettförlust; Kontrastiv Förlust; Generativa Modeller; Metrisk Inlärning; Latent Utrymme; Attributmanipulation;

   Abstract : Techniques for imposing a structure on the latent space of neural networks have seen much development in recent years. Clustering techniques used for classification have been used to great success, and with this work we hope to bridge the gap between contrastive losses and Generative models. READ MORE

4. 3. Cooperative security log analysis using machine learning : Analyzing different approaches to log featurization and classification

   University essay from Linköpings universitet/Databas och informationsteknik

   Author : Fredrik Malmfors; [2022]
   Keywords : Machine learning; word embeddings; deep learning; LSTM; CNN; auto encoder; NLP; natural language processing; intrusion detection; log analysis; logs; log classification; anomaly detection; supervised learning; unsupervised learning;

   Abstract : This thesis evaluates the performance of different machine learning approaches to log classification based on a dataset derived from simulating intrusive behavior towards an enterprise web application. The first experiment consists of performing attacks towards the web app in correlation with the logs to create a labeled dataset. READ MORE

5. 4. A Machine Learning Approach for Comprehending Cosmic Expansion

   University essay from KTH/Fysik

   Author : Ludvig Doeser; [2021]
   Keywords : Cosmology; Cosmic expansion; Galaxy Images; Machine learning; Kosmologi; kosmisk expansion; galaxbilder; maskininlärning;

   Abstract : This thesis aims at using novel machine learning techniques to test the dynamics of the Universe via the cosmological redshift-distance test. Currently, one of the most outstanding questions in cosmology is the physical cause of the accelerating cosmic expansion observed with supernovae. READ MORE

6. 5. Learning representations of features of fish for performing regression tasks

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Kristmundur Jónsson; [2021]
   Keywords : Representation learning; Deep Learning; regression; auto encoder; Variational auto encoder; Representationsinlärning; Djupinlärning; Regression; autoencoder; Variational autoencoder;

   Abstract : In the ever-changing landscape of the fishing industry, demands for automating specific processes are increasing substantially. Predicting future events eliminates much of the existing communication latency between fishing vessels and their customers and makes real-time analysis of onboard catch possible for the fishing industry. READ MORE